[WITHDRAWN] Deep Reinforcement Learning for Tehran Stock Trading
DOI:
https://doi.org/10.56705/ijodas.v3i3.53Keywords:
Machine Learning, Deep Learning, Reinforcement Learning, Deep Deterministic Policy Gradient, Actor-Critic, Stock tradingAbstract
One of the most interesting topics for research and also for making a profit is stock trading. Artificial intelligence has had a great impact on this path. A lot of research has been done to investigate the application of machine learning, and deep learning methods in stock trading. Despite the large amount of research done in the field of prediction and automation trading, stock trading as a deep reinforcement-learning problem remains an open research area. The progress of reinforcement learning as well as the intrinsic properties of reinforcement learning make it a suitable method for market trading in theory. In this paper, single stock trading models are presented based on the fine-tuned state-of-the-art deep reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Advantage Actor Critic (A2C)). These algorithms are able to interact with the trading market and capture the financial market dynamics.
The proposed models are compared, evaluated, and verified on historical stock trading data. Annualized return and Sharpe ratio have been used to evaluate the performance of proposed models. The results show that the agent designed based on both algorithms is able to make intelligent decisions on historical data. The DDPG strategy performs better than the A2C and achieves better results in terms of convergence, stability, and evaluation criteria.
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References
Fischer, T.G., 2018. Reinforcement learning in financial markets-a survey (No. 12/2018). FAU Discussion Papers in Economics.
Wang, Y., Wang, D., Zhang, S., Feng, Y., Li, S. and Zhou, Q., 2017. Deep Q-trading. cslt. riit. tsinghua. edu. cn.
Deng, Y., Bao, F., Kong, Y., Ren, Z. and Dai, Q., 2016. Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 28(3), pp.653-664.
Moody, J. and Saffell, M., 2001. Learning to trade via direct reinforcement. IEEE transactions on neural Networks, 12(4), pp.875-889.
Li, Y., Zheng, W. and Zheng, Z., 2019. Deep robust reinforcement learning for practical algorithmic trading. IEEE Access, 7, pp.108014-108022.
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D. and Riedmiller, M., 2014, January. Deterministic policy gradient algorithms. In International conference on machine learning (pp. 387-395). PMLR.
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D. and Wierstra, D., 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
Xiong, Z., Liu, X.Y., Zhong, S., Yang, H. and Walid, A., 2018. Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522.
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D. and Kavukcuoglu, K., 2016, June. Asynchronous methods for deep reinforcement learning. In International conference on machine learning (pp. 1928-1937). PMLR.
Sutton, R.S. and Barto, A.G., 2011. Reinforcement learning: An introduction.
Fischer, T.G., 2018. Reinforcement learning in financial markets-a survey (No. 12/2018). FAU Discussion Papers in Economics.
Silver, D., Hasselt, H., Hessel, M., Schaul, T., Guez, A., Harley, T., Dulac-Arnold, G., Reichert, D., Rabinowitz, N., Barreto, A. and Degris, T., 2017, July. The predictron: End-to-end learning and planning. In International Conference on Machine Learning (pp. 3191-3199). PMLR.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.
Konda, V. and Tsitsiklis, J., 1999. Actor-critic algorithms. Advances in neural information processing systems, 12.
Vitay, J., 2020. Deep Reinforcement Learning.
Cooper, I., 1996. Arithmetic versus geometric mean estimators: Setting discount rates for capital budgeting. European Financial Management, 2(2), pp.157-167.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. and Kudlur, M., 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J. and Zaremba, W., 2016. Openai gym. arXiv preprint arXiv:1606.01540.
https://stable-baselines.readthedocs.io/
Yang, H., Liu, X.Y., Zhong, S. and Walid, A., 2020, October. Deep reinforcement learning for automated stock trading: An ensemble strategy. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1-8).
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